GettingStarted

This README outlines the main resources to get started with research in our group. Other files and repositories contain information for more specific tasks.

Files:

• .gitignore is a sample file that you can include in your GitHub repositories to ignore local files (typically large files or files that can be recreated from the code) during a push to the remote repository.

Repositories

• salmon-ibm-model contains step-by-step instructions and a working example to setup OceanParcels on the HPC.

Creating a GitHub Account

Our group uses GitHub to share code, documentation, and for version control. If you do not already have an account, you should create one. If you want to refine your git skills, the OSU library offers git workshops, and the Ecampus Career Hub has a link to a thorough video introductions.

1. Create a GitHub account.
2. Request a PRO account for students, which will allow you to have private repositories.
3. Let us know your username so we can add you to the LagrangianOceanLab team.
4. Together, we will identify or create a private repository best suited for your work.

Setting up your project GitHub repository

In general, we limit the files we upload to GitHub to code, text documentation, and figures to include in our documentation. To avoid having to select which files we push individually every time, it is good practice to include a .gitignore file in each of our project repositories. A .gitignore file with several examples of file that could be ignored is included in this repository.

Working on the Jupyter Hub

Our group works on a shared Jupyter Hub for data visualization and analyses that do not require HPC resources. To better help each other, share data and model output, and facilitate project transitions, our team works out of a shared directory (./hpc/lol_scratch/). By default, only members of the lol_group can access the files in this directory, but you can change permissions as needed.

1. Access the Jupyter Hub at https://jupyter.ceoas.oregonstate.edu/, and log in with your OSU credentials.
2. Set up your GitHub SSH keys on the server. Your SSH keys should be saved in a subdirectory (typically ./.ssh/) of your home folder, so that they remain private and not accessible to members of our group.
3. Navigate to our group's directory ./hpc/lol_scratch/ and create a directory with your name or onid.
4. Clone the relevant GitHub project repository for your work under your new directory.

Important: ./hpc/lol_scratch/ is not backed up, so you should regularly push your work to GitHub.

Working on the HPC

You must first fill out this form to request an account on Wildwood and sign up for an orientation with Chris Sullivan. Those orientations are tailored to your skills and objectives, so feel free to discuss with other group members what they have found useful.

To access Wildwood, ssh using your university credentials:

ssh -Y [email protected]

Your home directory should include soft links to our storage spaces, Garwood_Lab and lol_scratch (see next section).

Storage spaces & workflow

Our group has access to the following two storage spaces:

• Garwood_Lab which is backed up in two locations with daily (?) snapshots but has a slow connection to our network
• lol_scratch which is not backed up but has a faster connection to our network

The recommended workflow is to run simulations reading & writing to lol_scratch and copy all output files to Garwood_Lab once the simulation has completed and we are satisfied that the files need to be kept. Input files should be stored on Garwood_Lab and copied on lol_scratch when they are actively used.

Although visualizations often do not require the fastest connection, the Jupyter Hub is currently only connected to lol_scratch, meaning that the files must be left in that space to be analyzed. The structure of our storage is likely to change in the upcoming years.

Using conda

1. Install Miniconda.

Installing Miniconda is preferable to installing Anaconda because its packages include only conda and Python, which limits the storage it requires. Instead, users install only the package they need.

2. Create a conda environment for your project

One of the advantages of conda is that you can create conda environments, which keeps modules for different projects separate, avoiding potential conflicts. It is also possible for people to recreate someone else's conda environment, ensuring reproducibility. I like to have an environment named sandbox for data exploration, etc. It is completely fine if that environment breaks.

From now on, when you want to work on a project or install python modules, activate the conda environment you created:

conda activate nameoftheenvironment

Installing Jupyter Lab and other Python modules locally

Once you have conda installed, open Terminal, activate the relevant conda environment (if applicable) and install Jupyter Lab:

conda install -c conda-forge jupyterlab

Using the same command, you can install Python modules commonly used in oceanography:

• xarray
• cartopy
• numpy
• pandas
• scipy
• matplotlib

Setting up your computer for easily accessing servers

To easily access a remote server, you can set up ssh-keys on your local computer. It is best practice to create a new set of keys for each computer/server combination, so that you can easily deactivate a key if it becomes compromised. The instructions on DigitalOcean and GitHub are two great resources for creating ssh-keys.

Note that you should use the ed25519 signature system rather than the rsa system mentioned on DigitalOcean, and use an informative filename when saving them (e.g., I include the name of the server):

ssh-keygen -t ed25519

Download useful tools

Visual Studio Code

VSCode is a versatile code editor. For instance, it offers markdown previews to help edit this README.md on your local computer. On a Mac, pressing command + k followed by pressing v will open a side-by-side markdown preview.

iTerm2

iTerm2 is highly customizable Terminal application. One of my favorite features is to create profiles for each server that I access, which allows me to easily know where I am in a Terminal window. I detailed how to setup profiles across platform in the scientific-workflow repository maintained by Jesse Cusack.